Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Inter-slice radio resource allocation (IS-RRA) in the radio access network (RAN) is very important. However, user mobility brings new challenges for optimal IS-RRA. This paper first proposes a soft and hard hybrid slicing framework where a common slice is introduced to realize a trade-off between isolation and spectrum efficiency (SE). To address the challenges posed by user mobility, we propose a two-step deep learning-based algorithm: joint long short-term memory (LSTM)-based network state prediction and deep Q network (DQN)-based slicing strategy. In the proposal, LSTM networks are employed to predict traffic demand and the location of each user in a slicing window level. Moreover, channel gain is mapped by location and a radio map. Then, the predicted channel gain and traffic demand are input to the DQN to output the precise slicing adjustment. Finally, experiment results confirm the effectiveness of our proposed slicing framework: the slices' SLA can be guaranteed well, and the proposed algorithm can achieve near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and SE.
翻译:网络切片是保证5G和未来网络中各种服务级别协议(SLA)的关键驱动力,无线电接入网络中的隔热无线电资源分配(IS-RRA)非常重要,但用户的流动性为最佳IS-RRA带来了新的挑战。本文首先提出软硬混合切片框架,在这种框架中引入了一个共同的切片,以实现孤立与频谱效率之间的权衡。为了应对用户流动性带来的挑战,我们提议了一个两步深层次的深层次学习算法:在无线电接入网络(RAN)中进行基于长期记忆的网络状态预测和基于深度Q网络的剪切片战略。在提案中,使用LSTM网络来预测交通需求和每个用户在切片窗口水平上的位置。此外,通过地点和无线电图绘制了通道收益图。然后,预测的频道收益和交通需求被输入DQN,以得出准确的切片调整结果。最后,实验结果证实了我们提议的剪片框架的有效性:在SLA的切片与SLA的隔离度上可以保证准确的满意度,而拟议的SLA的绩效可以达到接近的水平。